A predictive phase locked loop applicable to utility and non-utility AC power systems
Bibliographic record
Abstract
A predictive phase locked loop (PPLL) suitable for applications in utility and non-utility power systems and power electronics is presented. The PPLL is frequency adaptive and can provide time variant information about the frequency and amplitude of the fundamental component of an input signal. The PPLL offers a high degree of immunity to wide-band noise, harmonics, inter-harmonics and impulse disturbances. Analytical methods for modeling the PPLL are developed to achieve high execution speed and low real-estate utilization. The mathematical properties of the analytical methods are presented. The PPLL is implemented on a field programmable gate array (FPGA). The locking range of the PPLL is from a fraction of Hz to a few kHz and from 3% to 100% of the nominal input amplitude. The worst case response time of the PPLL is 2 cycles of the input signal period for any realistic perturbation in frequency, amplitude, and/or phase angle. The proposed method is faster, more flexible and more robust than currently available methods
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".